{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pickle as pk\n",
        "X_, y_ = pk.load(open('/content/ppg_spec_maps.unknown','rb'))"
      ],
      "metadata": {
        "id": "M9OqYBU4OFLu"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ne73KDDrNgn9",
        "outputId": "78df9389-0dd2-4758-baf4-201ba0b76593"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ZIydPnhQRkT//+c+yb98+mTVr1nUzgUBAmpqaOt0AAMDtK6wzF8uXL5empiYZMWKEuN1uCQaD8tRTT8n8+fOvmyktLZXVq1erBwUAALEhrDMXL7zwgmzdulW2bdsmR44ckS1btsiPfvQj2bJly3UzJSUl4vf7O27V1dXqoQEAQPQK68zF448/LsuXL5f7779fRETGjBkjZ8+eldLSUikqKrpmxuv1itfr1U8KAABiQlhnLlpaWsTl6hxxu90SCoWsDgUAAGJXWGcu7r33XnnqqackNzdXRo0aJUePHpW1a9fKAw880F3zAQCAGBNWuXjuuedkxYoV8tBDD0lDQ4NkZWXJt771LVm5cmV3zQcAAGJMWOXC5/PJunXrZN26dd00DgAAiHVcWwQAAFgV1pkLmxy3WxzH3bVwIKA69rANum+H/VLrd1V5uVMX71PjUeXf/+ZnVPl+r+quJZN4skaVb5w9VpU3Daq4JJWdVOXjJw1R5T8cGa/KX07VPe0PPrNBlc/7/X+o8qmb3lblQy5Hldcy7e26vFf38Xf3TdEdv/GsKi/uLr7u/4O36rzu+EHdNyCELl/WHV/57xeXMh8K6vK3iDMXAADAKsoFAACwinIBAACsolwAAACrKBcAAMAqygUAALCKcgEAAKyiXAAAAKsoFwAAwCrKBQAAsIpyAQAArKJcAAAAqygXAADAKsoFAACwqscvuW7Mx5frbjdtXf47XOaKagYnpLtkezDQqsprtbfrjh8M6C45Hbyiu+R6u3L929t0//6Q8sPXHtLtP+38wYDuksnaj1/TRe0lq3X/fs1rh4iIYyJ7yXXH6C6ZbYK654/RPv+U6y/K40tIt/+0+ZDy848oP/4h7fqbrr9+tMvHx/7k8/iNOOZWHmXRe++9Jzk5OT15SAAAYEl1dbVkZ2ff8DE9Xi5CoZDU1NSIz+cTx7n6fxBNTU2Sk5Mj1dXVkpSU1JOj3RZYPx3WT4f102H9dFg/nZutnzFGLl68KFlZWeJy3fhdFT3+ZRGXy3XTxiMikpSUxOZQYP10WD8d1k+H9dNh/XRutH7Jycm39Hfwhk4AAGAV5QIAAFgVdeXC6/XKqlWrxOv1RnqUmMT66bB+OqyfDuunw/rp2Fy/Hn9DJwAAuL1F3ZkLAAAQ2ygXAADAKsoFAACwinIBAACsolwAAACroqpcrF+/XgYNGiS9evWS/Px8OXToUKRHignf//73xXGcTrcRI0ZEeqyotXfvXrn33nslKytLHMeR3/zmN53+3BgjK1eulMzMTElISJDCwkI5depUZIaNQjdbv2984xtX7ceZM2dGZtgoVFpaKpMmTRKfzydpaWly3333SWVlZafHtLa2SnFxsfTv31/69OkjX/nKV6S+vj5CE0eXW1m/u++++6o9+OCDD0Zo4uiyYcMGGTt2bMdP4SwoKJCXX365489t7b2oKRc7d+6UZcuWyapVq+TIkSMybtw4mTFjhjQ0NER6tJgwatQoqa2t7bjt27cv0iNFrebmZhk3bpysX7/+mn/+7LPPyk9+8hPZuHGjHDx4UHr37i0zZsyQ1tbIXg03Wtxs/UREZs6c2Wk/bt++vQcnjG7l5eVSXFwsBw4ckNdee03a2tpk+vTp0tzc3PGYRx99VH73u9/Jiy++KOXl5VJTUyNz586N4NTR41bWT0Rk4cKFnfbgs88+G6GJo0t2drasWbNGKioq5PDhw/KlL31J5syZI++8846IWNx7JkpMnjzZFBcXd/w+GAyarKwsU1paGsGpYsOqVavMuHHjIj1GTBIRs2vXro7fh0Ihk5GRYX74wx923NfY2Gi8Xq/Zvn17BCaMbp9eP2OMKSoqMnPmzInIPLGooaHBiIgpLy83xny83+Lj482LL77Y8Zi//vWvRkTM/v37IzVm1Pr0+hljzBe+8AXzyCOPRG6oGNO3b1/zi1/8wurei4ozF1euXJGKigopLCzsuM/lcklhYaHs378/gpPFjlOnTklWVpYMHjxY5s+fL+fOnYv0SDGpqqpK6urqOu3F5ORkyc/PZy+GoaysTNLS0mT48OGyePFi+fDDDyM9UtTy+/0iItKvXz8REamoqJC2trZOe3DEiBGSm5vLHryGT6/fJ7Zu3SqpqakyevRoKSkpkZaWlkiMF9WCwaDs2LFDmpubpaCgwOre6/Grol7L+fPnJRgMSnp6eqf709PT5W9/+1uEpood+fn5snnzZhk+fLjU1tbK6tWr5fOf/7ycOHFCfD5fpMeLKXV1dSIi19yLn/wZbmzmzJkyd+5cycvLkzNnzsj3vvc9mTVrluzfv1/cbnekx4sqoVBIli5dKlOmTJHRo0eLyMd70OPxSEpKSqfHsgevdq31ExH52te+JgMHDpSsrCw5fvy4PPHEE1JZWSm//vWvIzht9Hj77beloKBAWltbpU+fPrJr1y4ZOXKkHDt2zNrei4pyAZ1Zs2Z1/Hrs2LGSn58vAwcOlBdeeEG++c1vRnAy/Cu6//77O349ZswYGTt2rAwZMkTKyspk2rRpEZws+hQXF8uJEyd4j1QXXW/9Fi1a1PHrMWPGSGZmpkybNk3OnDkjQ4YM6ekxo87w4cPl2LFj4vf75Ve/+pUUFRVJeXm51WNExZdFUlNTxe12X/WO1Pr6esnIyIjQVLErJSVF7rzzTjl9+nSkR4k5n+w39qI9gwcPltTUVPbjpyxZskReeukleeONNyQ7O7vj/oyMDLly5Yo0NjZ2ejx7sLPrrd+15Ofni4iwB//B4/HI0KFDZcKECVJaWirjxo2TH//4x1b3XlSUC4/HIxMmTJA9e/Z03BcKhWTPnj1SUFAQwcli06VLl+TMmTOSmZkZ6VFiTl5enmRkZHTai01NTXLw4EH2Yhe999578uGHH7If/8EYI0uWLJFdu3bJ66+/Lnl5eZ3+fMKECRIfH99pD1ZWVsq5c+fYg3Lz9buWY8eOiYiwB68jFApJIBCwu/fsvue063bs2GG8Xq/ZvHmz+ctf/mIWLVpkUlJSTF1dXaRHi3rf+c53TFlZmamqqjJvvvmmKSwsNKmpqaahoSHSo0WlixcvmqNHj5qjR48aETFr1641R48eNWfPnjXGGLNmzRqTkpJidu/ebY4fP27mzJlj8vLyzOXLlyM8eXS40fpdvHjRPPbYY2b//v2mqqrK/PGPfzTjx483w4YNM62trZEePSosXrzYJCcnm7KyMlNbW9txa2lp6XjMgw8+aHJzc83rr79uDh8+bAoKCkxBQUEEp44eN1u/06dPmx/84Afm8OHDpqqqyuzevdsMHjzYTJ06NcKTR4fly5eb8vJyU1VVZY4fP26WL19uHMcxf/jDH4wx9vZe1JQLY4x57rnnTG5urvF4PGby5MnmwIEDkR4pJsybN89kZmYaj8dj7rjjDjNv3jxz+vTpSI8Vtd544w0jIlfdioqKjDEffzvqihUrTHp6uvF6vWbatGmmsrIyskNHkRutX0tLi5k+fboZMGCAiY+PNwMHDjQLFy7kPwn/5FprJyLm+eef73jM5cuXzUMPPWT69u1rEhMTzZe//GVTW1sbuaGjyM3W79y5c2bq1KmmX79+xuv1mqFDh5rHH3/c+P3+yA4eJR544AEzcOBA4/F4zIABA8y0adM6ioUx9vaeY4wxXTyTAgAAcJWoeM8FAAC4fVAuAACAVZQLAABgFeUCAABYRbkAAABWUS4AAIBVlAsAAGAV5QIAAFhFuQAAAFZRLgAAgFWUCwAAYNX/AXxcVn6wFUUKAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# prompt: show the variable x_ x_ is a set of images\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "for image in X_[:10]:\n",
        "  plt.imshow(image)\n",
        "  plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKtUQTjFVTKF",
        "outputId": "d79d1ed7-e533-41e3-ab43-2c7572eab327"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(431, 10, 31, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sc17cWSwSlGJ",
        "outputId": "611c3bfe-dc48-421e-dc7c-46241c558db3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9461188534749133"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_, y_, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=10,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    zoom_range=0.1,\n",
        ")\n",
        "\n",
        "\n",
        "datagen.fit(X_train)\n",
        "\n",
        "augmentation_factor = 5\n",
        "\n",
        "\n",
        "augmented_images = []\n",
        "augmented_labels = []\n",
        "\n",
        "for i in range(len(X_train)):\n",
        "    img = X_train[i].reshape((1, *X_train[i].shape))\n",
        "    label = y_train[i]\n",
        "    counter = 0\n",
        "    for batch in datagen.flow(img, batch_size=1):\n",
        "        augmented_images.append(batch[0])\n",
        "        augmented_labels.append(label)\n",
        "        counter += 1\n",
        "        if counter >= augmentation_factor:\n",
        "            break\n",
        "\n",
        "\n",
        "augmented_images = np.array(augmented_images)\n",
        "augmented_labels = np.array(augmented_labels)\n",
        "\n",
        "X_train_augmented = np.concatenate((X_train, augmented_images), axis=0)\n",
        "y_train_augmented = np.concatenate((y_train, augmented_labels), axis=0)\n"
      ],
      "metadata": {
        "id": "C0GPanQvSuWY"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_augmented.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FK_SiwPJXL3y",
        "outputId": "64389b32-aa09-44a0-f1e2-6766e9425c88"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2064, 10, 31, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_augmented.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVznNF6zZwH2",
        "outputId": "36cccb87-78d0-487d-f0c4-338c2941656d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2064,)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X_train_augmented, y_train_augmented, test_size=0.2, random_state=42)\n",
        "\n"
      ],
      "metadata": {
        "id": "i0AN7YrjoHlU"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dropout, Conv2D, MaxPooling2D, Flatten, Dense\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "# Define input shape based on your data\n",
        "input_shape = (10, 31, 1)\n",
        "\n",
        "model = Sequential([\n",
        "    Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Dropout(0.5),  # Increase dropout rate\n",
        "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Dropout(0.5),  # Increase dropout rate\n",
        "    Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Dropout(0.5),  # Increase dropout rate\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.7),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Fit the model on training data\n",
        "history = model.fit(X_train_augmented, y_train_augmented, epochs=200, batch_size=32, validation_data=(X_val, y_val))\n",
        "\n",
        "# Evaluate the model on test data (optional)\n",
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(f'Test Loss: {loss:.4f}')\n",
        "print(f'Test Accuracy: {accuracy:.4f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8xZkWBfiOaZ",
        "outputId": "2e593847-1152-4ff2-b07f-f2a534f132d3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "65/65 [==============================] - 3s 30ms/step - loss: 0.6944 - accuracy: 0.5194 - val_loss: 0.6895 - val_accuracy: 0.5351\n",
            "Epoch 2/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6912 - accuracy: 0.5237 - val_loss: 0.6890 - val_accuracy: 0.5351\n",
            "Epoch 3/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6908 - accuracy: 0.5281 - val_loss: 0.6875 - val_accuracy: 0.5351\n",
            "Epoch 4/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6926 - accuracy: 0.5160 - val_loss: 0.6918 - val_accuracy: 0.5593\n",
            "Epoch 5/200\n",
            "65/65 [==============================] - 2s 26ms/step - loss: 0.6907 - accuracy: 0.5252 - val_loss: 0.6885 - val_accuracy: 0.5642\n",
            "Epoch 6/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.6919 - accuracy: 0.5189 - val_loss: 0.6861 - val_accuracy: 0.5496\n",
            "Epoch 7/200\n",
            "65/65 [==============================] - 2s 32ms/step - loss: 0.6906 - accuracy: 0.5329 - val_loss: 0.6839 - val_accuracy: 0.5787\n",
            "Epoch 8/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6874 - accuracy: 0.5339 - val_loss: 0.6825 - val_accuracy: 0.5787\n",
            "Epoch 9/200\n",
            "65/65 [==============================] - 1s 21ms/step - loss: 0.6866 - accuracy: 0.5426 - val_loss: 0.6795 - val_accuracy: 0.6102\n",
            "Epoch 10/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6852 - accuracy: 0.5601 - val_loss: 0.6745 - val_accuracy: 0.5981\n",
            "Epoch 11/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6808 - accuracy: 0.5552 - val_loss: 0.6670 - val_accuracy: 0.5714\n",
            "Epoch 12/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6831 - accuracy: 0.5693 - val_loss: 0.6691 - val_accuracy: 0.6053\n",
            "Epoch 13/200\n",
            "65/65 [==============================] - 1s 21ms/step - loss: 0.6844 - accuracy: 0.5606 - val_loss: 0.6687 - val_accuracy: 0.6271\n",
            "Epoch 14/200\n",
            "65/65 [==============================] - 2s 29ms/step - loss: 0.6809 - accuracy: 0.5673 - val_loss: 0.6617 - val_accuracy: 0.6271\n",
            "Epoch 15/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.6678 - accuracy: 0.5979 - val_loss: 0.6545 - val_accuracy: 0.6247\n",
            "Epoch 16/200\n",
            "65/65 [==============================] - 2s 26ms/step - loss: 0.6708 - accuracy: 0.5770 - val_loss: 0.6553 - val_accuracy: 0.6320\n",
            "Epoch 17/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6747 - accuracy: 0.5848 - val_loss: 0.6508 - val_accuracy: 0.6441\n",
            "Epoch 18/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6766 - accuracy: 0.5736 - val_loss: 0.6555 - val_accuracy: 0.6174\n",
            "Epoch 19/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6705 - accuracy: 0.5935 - val_loss: 0.6572 - val_accuracy: 0.6416\n",
            "Epoch 20/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.6754 - accuracy: 0.5916 - val_loss: 0.6592 - val_accuracy: 0.6877\n",
            "Epoch 21/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6695 - accuracy: 0.5862 - val_loss: 0.6435 - val_accuracy: 0.6538\n",
            "Epoch 22/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6663 - accuracy: 0.6008 - val_loss: 0.6428 - val_accuracy: 0.6344\n",
            "Epoch 23/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.6713 - accuracy: 0.6061 - val_loss: 0.6414 - val_accuracy: 0.6562\n",
            "Epoch 24/200\n",
            "65/65 [==============================] - 2s 35ms/step - loss: 0.6703 - accuracy: 0.5950 - val_loss: 0.6462 - val_accuracy: 0.6707\n",
            "Epoch 25/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6634 - accuracy: 0.6037 - val_loss: 0.6346 - val_accuracy: 0.6731\n",
            "Epoch 26/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6553 - accuracy: 0.6037 - val_loss: 0.6356 - val_accuracy: 0.6441\n",
            "Epoch 27/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6624 - accuracy: 0.5921 - val_loss: 0.6403 - val_accuracy: 0.6538\n",
            "Epoch 28/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6648 - accuracy: 0.6027 - val_loss: 0.6371 - val_accuracy: 0.6901\n",
            "Epoch 29/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6598 - accuracy: 0.6100 - val_loss: 0.6295 - val_accuracy: 0.7143\n",
            "Epoch 30/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6563 - accuracy: 0.6143 - val_loss: 0.6252 - val_accuracy: 0.6973\n",
            "Epoch 31/200\n",
            "65/65 [==============================] - 2s 29ms/step - loss: 0.6600 - accuracy: 0.6090 - val_loss: 0.6336 - val_accuracy: 0.7119\n",
            "Epoch 32/200\n",
            "65/65 [==============================] - 3s 42ms/step - loss: 0.6445 - accuracy: 0.6158 - val_loss: 0.6072 - val_accuracy: 0.7288\n",
            "Epoch 33/200\n",
            "65/65 [==============================] - 3s 42ms/step - loss: 0.6604 - accuracy: 0.6313 - val_loss: 0.6173 - val_accuracy: 0.7361\n",
            "Epoch 34/200\n",
            "65/65 [==============================] - 2s 28ms/step - loss: 0.6537 - accuracy: 0.6197 - val_loss: 0.6226 - val_accuracy: 0.6925\n",
            "Epoch 35/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6423 - accuracy: 0.6274 - val_loss: 0.6182 - val_accuracy: 0.7288\n",
            "Epoch 36/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6418 - accuracy: 0.6255 - val_loss: 0.6133 - val_accuracy: 0.7094\n",
            "Epoch 37/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6445 - accuracy: 0.6424 - val_loss: 0.6255 - val_accuracy: 0.6877\n",
            "Epoch 38/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6426 - accuracy: 0.6289 - val_loss: 0.6028 - val_accuracy: 0.7191\n",
            "Epoch 39/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6412 - accuracy: 0.6206 - val_loss: 0.5999 - val_accuracy: 0.7143\n",
            "Epoch 40/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.6396 - accuracy: 0.6342 - val_loss: 0.5917 - val_accuracy: 0.7288\n",
            "Epoch 41/200\n",
            "65/65 [==============================] - 2s 37ms/step - loss: 0.6328 - accuracy: 0.6405 - val_loss: 0.5850 - val_accuracy: 0.7482\n",
            "Epoch 42/200\n",
            "65/65 [==============================] - 2s 30ms/step - loss: 0.6363 - accuracy: 0.6434 - val_loss: 0.5922 - val_accuracy: 0.7022\n",
            "Epoch 43/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6464 - accuracy: 0.6129 - val_loss: 0.6185 - val_accuracy: 0.6852\n",
            "Epoch 44/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.6406 - accuracy: 0.6294 - val_loss: 0.5950 - val_accuracy: 0.7312\n",
            "Epoch 45/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6297 - accuracy: 0.6584 - val_loss: 0.5766 - val_accuracy: 0.7676\n",
            "Epoch 46/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.6232 - accuracy: 0.6449 - val_loss: 0.5611 - val_accuracy: 0.7700\n",
            "Epoch 47/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6245 - accuracy: 0.6463 - val_loss: 0.5682 - val_accuracy: 0.7530\n",
            "Epoch 48/200\n",
            "65/65 [==============================] - 2s 30ms/step - loss: 0.6314 - accuracy: 0.6507 - val_loss: 0.5651 - val_accuracy: 0.7603\n",
            "Epoch 49/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.6222 - accuracy: 0.6584 - val_loss: 0.6017 - val_accuracy: 0.6634\n",
            "Epoch 50/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.6083 - accuracy: 0.6575 - val_loss: 0.5487 - val_accuracy: 0.7603\n",
            "Epoch 51/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6178 - accuracy: 0.6512 - val_loss: 0.5529 - val_accuracy: 0.7506\n",
            "Epoch 52/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.6205 - accuracy: 0.6560 - val_loss: 0.5499 - val_accuracy: 0.7748\n",
            "Epoch 53/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6171 - accuracy: 0.6497 - val_loss: 0.5501 - val_accuracy: 0.7530\n",
            "Epoch 54/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6059 - accuracy: 0.6744 - val_loss: 0.5465 - val_accuracy: 0.7748\n",
            "Epoch 55/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6041 - accuracy: 0.6642 - val_loss: 0.5558 - val_accuracy: 0.7821\n",
            "Epoch 56/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6152 - accuracy: 0.6589 - val_loss: 0.5465 - val_accuracy: 0.7845\n",
            "Epoch 57/200\n",
            "65/65 [==============================] - 2s 25ms/step - loss: 0.6080 - accuracy: 0.6604 - val_loss: 0.5386 - val_accuracy: 0.7869\n",
            "Epoch 58/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.6165 - accuracy: 0.6579 - val_loss: 0.5483 - val_accuracy: 0.7821\n",
            "Epoch 59/200\n",
            "65/65 [==============================] - 2s 34ms/step - loss: 0.6166 - accuracy: 0.6638 - val_loss: 0.5557 - val_accuracy: 0.7797\n",
            "Epoch 60/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6089 - accuracy: 0.6686 - val_loss: 0.5289 - val_accuracy: 0.7893\n",
            "Epoch 61/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5951 - accuracy: 0.6715 - val_loss: 0.5242 - val_accuracy: 0.7651\n",
            "Epoch 62/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5807 - accuracy: 0.7006 - val_loss: 0.5286 - val_accuracy: 0.7409\n",
            "Epoch 63/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.6099 - accuracy: 0.6570 - val_loss: 0.5477 - val_accuracy: 0.7893\n",
            "Epoch 64/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5875 - accuracy: 0.6807 - val_loss: 0.5132 - val_accuracy: 0.7772\n",
            "Epoch 65/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.6071 - accuracy: 0.6759 - val_loss: 0.5371 - val_accuracy: 0.7845\n",
            "Epoch 66/200\n",
            "65/65 [==============================] - 2s 29ms/step - loss: 0.5843 - accuracy: 0.6827 - val_loss: 0.5262 - val_accuracy: 0.7724\n",
            "Epoch 67/200\n",
            "65/65 [==============================] - 2s 37ms/step - loss: 0.5812 - accuracy: 0.6943 - val_loss: 0.5131 - val_accuracy: 0.7651\n",
            "Epoch 68/200\n",
            "65/65 [==============================] - 2s 25ms/step - loss: 0.5824 - accuracy: 0.6996 - val_loss: 0.5024 - val_accuracy: 0.8087\n",
            "Epoch 69/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5839 - accuracy: 0.6831 - val_loss: 0.4813 - val_accuracy: 0.8378\n",
            "Epoch 70/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5863 - accuracy: 0.6904 - val_loss: 0.5032 - val_accuracy: 0.7797\n",
            "Epoch 71/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5912 - accuracy: 0.6797 - val_loss: 0.4955 - val_accuracy: 0.8087\n",
            "Epoch 72/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5767 - accuracy: 0.7059 - val_loss: 0.4861 - val_accuracy: 0.8257\n",
            "Epoch 73/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5784 - accuracy: 0.6827 - val_loss: 0.4887 - val_accuracy: 0.8329\n",
            "Epoch 74/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5734 - accuracy: 0.6870 - val_loss: 0.4838 - val_accuracy: 0.8523\n",
            "Epoch 75/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.5727 - accuracy: 0.6972 - val_loss: 0.4781 - val_accuracy: 0.8329\n",
            "Epoch 76/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.5711 - accuracy: 0.7006 - val_loss: 0.4710 - val_accuracy: 0.8475\n",
            "Epoch 77/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5605 - accuracy: 0.7064 - val_loss: 0.4671 - val_accuracy: 0.8402\n",
            "Epoch 78/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5745 - accuracy: 0.7045 - val_loss: 0.4684 - val_accuracy: 0.8426\n",
            "Epoch 79/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5532 - accuracy: 0.7030 - val_loss: 0.4596 - val_accuracy: 0.8571\n",
            "Epoch 80/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5699 - accuracy: 0.7001 - val_loss: 0.4752 - val_accuracy: 0.8426\n",
            "Epoch 81/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5660 - accuracy: 0.6996 - val_loss: 0.4511 - val_accuracy: 0.8499\n",
            "Epoch 82/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5609 - accuracy: 0.7025 - val_loss: 0.4468 - val_accuracy: 0.8596\n",
            "Epoch 83/200\n",
            "65/65 [==============================] - 2s 28ms/step - loss: 0.5405 - accuracy: 0.7297 - val_loss: 0.4247 - val_accuracy: 0.8644\n",
            "Epoch 84/200\n",
            "65/65 [==============================] - 2s 35ms/step - loss: 0.5708 - accuracy: 0.7035 - val_loss: 0.4501 - val_accuracy: 0.8571\n",
            "Epoch 85/200\n",
            "65/65 [==============================] - 2s 31ms/step - loss: 0.5661 - accuracy: 0.7016 - val_loss: 0.4506 - val_accuracy: 0.8644\n",
            "Epoch 86/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5600 - accuracy: 0.7054 - val_loss: 0.4392 - val_accuracy: 0.8644\n",
            "Epoch 87/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5347 - accuracy: 0.7263 - val_loss: 0.4489 - val_accuracy: 0.8402\n",
            "Epoch 88/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5680 - accuracy: 0.7016 - val_loss: 0.4488 - val_accuracy: 0.8523\n",
            "Epoch 89/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5374 - accuracy: 0.7330 - val_loss: 0.4317 - val_accuracy: 0.8692\n",
            "Epoch 90/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5524 - accuracy: 0.7171 - val_loss: 0.4430 - val_accuracy: 0.8523\n",
            "Epoch 91/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5518 - accuracy: 0.7171 - val_loss: 0.4619 - val_accuracy: 0.8523\n",
            "Epoch 92/200\n",
            "65/65 [==============================] - 2s 31ms/step - loss: 0.5524 - accuracy: 0.7200 - val_loss: 0.4264 - val_accuracy: 0.8862\n",
            "Epoch 93/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.5485 - accuracy: 0.7171 - val_loss: 0.4291 - val_accuracy: 0.8814\n",
            "Epoch 94/200\n",
            "65/65 [==============================] - 2s 27ms/step - loss: 0.5388 - accuracy: 0.7272 - val_loss: 0.4146 - val_accuracy: 0.8862\n",
            "Epoch 95/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5586 - accuracy: 0.7156 - val_loss: 0.4554 - val_accuracy: 0.8450\n",
            "Epoch 96/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5492 - accuracy: 0.7209 - val_loss: 0.4395 - val_accuracy: 0.8523\n",
            "Epoch 97/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5405 - accuracy: 0.7321 - val_loss: 0.4240 - val_accuracy: 0.8596\n",
            "Epoch 98/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5418 - accuracy: 0.7297 - val_loss: 0.4121 - val_accuracy: 0.8765\n",
            "Epoch 99/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5265 - accuracy: 0.7330 - val_loss: 0.3905 - val_accuracy: 0.8935\n",
            "Epoch 100/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5274 - accuracy: 0.7374 - val_loss: 0.3989 - val_accuracy: 0.8741\n",
            "Epoch 101/200\n",
            "65/65 [==============================] - 2s 37ms/step - loss: 0.5399 - accuracy: 0.7292 - val_loss: 0.4051 - val_accuracy: 0.8862\n",
            "Epoch 102/200\n",
            "65/65 [==============================] - 2s 34ms/step - loss: 0.5104 - accuracy: 0.7611 - val_loss: 0.3853 - val_accuracy: 0.8862\n",
            "Epoch 103/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5403 - accuracy: 0.7267 - val_loss: 0.3972 - val_accuracy: 0.8838\n",
            "Epoch 104/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5308 - accuracy: 0.7369 - val_loss: 0.3932 - val_accuracy: 0.8814\n",
            "Epoch 105/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5276 - accuracy: 0.7301 - val_loss: 0.3930 - val_accuracy: 0.8814\n",
            "Epoch 106/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5075 - accuracy: 0.7418 - val_loss: 0.3794 - val_accuracy: 0.8838\n",
            "Epoch 107/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.5287 - accuracy: 0.7374 - val_loss: 0.3832 - val_accuracy: 0.8910\n",
            "Epoch 108/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5369 - accuracy: 0.7287 - val_loss: 0.3968 - val_accuracy: 0.9056\n",
            "Epoch 109/200\n",
            "65/65 [==============================] - 2s 29ms/step - loss: 0.5264 - accuracy: 0.7374 - val_loss: 0.3766 - val_accuracy: 0.9104\n",
            "Epoch 110/200\n",
            "65/65 [==============================] - 3s 39ms/step - loss: 0.5209 - accuracy: 0.7379 - val_loss: 0.3611 - val_accuracy: 0.9201\n",
            "Epoch 111/200\n",
            "65/65 [==============================] - 2s 31ms/step - loss: 0.5240 - accuracy: 0.7485 - val_loss: 0.3712 - val_accuracy: 0.9128\n",
            "Epoch 112/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5097 - accuracy: 0.7568 - val_loss: 0.3873 - val_accuracy: 0.8814\n",
            "Epoch 113/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5202 - accuracy: 0.7481 - val_loss: 0.3663 - val_accuracy: 0.9031\n",
            "Epoch 114/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.5094 - accuracy: 0.7461 - val_loss: 0.3933 - val_accuracy: 0.8620\n",
            "Epoch 115/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.5066 - accuracy: 0.7563 - val_loss: 0.3797 - val_accuracy: 0.9031\n",
            "Epoch 116/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5162 - accuracy: 0.7553 - val_loss: 0.3692 - val_accuracy: 0.9031\n",
            "Epoch 117/200\n",
            "65/65 [==============================] - 2s 31ms/step - loss: 0.5237 - accuracy: 0.7427 - val_loss: 0.3849 - val_accuracy: 0.8814\n",
            "Epoch 118/200\n",
            "65/65 [==============================] - 2s 37ms/step - loss: 0.5125 - accuracy: 0.7452 - val_loss: 0.3547 - val_accuracy: 0.9225\n",
            "Epoch 119/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4993 - accuracy: 0.7544 - val_loss: 0.3422 - val_accuracy: 0.9177\n",
            "Epoch 120/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4874 - accuracy: 0.7699 - val_loss: 0.3594 - val_accuracy: 0.9153\n",
            "Epoch 121/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4864 - accuracy: 0.7665 - val_loss: 0.3518 - val_accuracy: 0.8983\n",
            "Epoch 122/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5052 - accuracy: 0.7403 - val_loss: 0.3535 - val_accuracy: 0.9225\n",
            "Epoch 123/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5077 - accuracy: 0.7573 - val_loss: 0.3647 - val_accuracy: 0.9104\n",
            "Epoch 124/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.5074 - accuracy: 0.7500 - val_loss: 0.3496 - val_accuracy: 0.9274\n",
            "Epoch 125/200\n",
            "65/65 [==============================] - 2s 27ms/step - loss: 0.4992 - accuracy: 0.7461 - val_loss: 0.3612 - val_accuracy: 0.9031\n",
            "Epoch 126/200\n",
            "65/65 [==============================] - 2s 37ms/step - loss: 0.5011 - accuracy: 0.7563 - val_loss: 0.3467 - val_accuracy: 0.9080\n",
            "Epoch 127/200\n",
            "65/65 [==============================] - 2s 32ms/step - loss: 0.4893 - accuracy: 0.7558 - val_loss: 0.3416 - val_accuracy: 0.9056\n",
            "Epoch 128/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.5083 - accuracy: 0.7519 - val_loss: 0.3824 - val_accuracy: 0.9080\n",
            "Epoch 129/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4997 - accuracy: 0.7476 - val_loss: 0.3535 - val_accuracy: 0.9128\n",
            "Epoch 130/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4906 - accuracy: 0.7578 - val_loss: 0.3647 - val_accuracy: 0.8814\n",
            "Epoch 131/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.5287 - accuracy: 0.7379 - val_loss: 0.3795 - val_accuracy: 0.9201\n",
            "Epoch 132/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4884 - accuracy: 0.7534 - val_loss: 0.3555 - val_accuracy: 0.9128\n",
            "Epoch 133/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4924 - accuracy: 0.7699 - val_loss: 0.3274 - val_accuracy: 0.9249\n",
            "Epoch 134/200\n",
            "65/65 [==============================] - 2s 33ms/step - loss: 0.4961 - accuracy: 0.7490 - val_loss: 0.3394 - val_accuracy: 0.9370\n",
            "Epoch 135/200\n",
            "65/65 [==============================] - 2s 35ms/step - loss: 0.5006 - accuracy: 0.7519 - val_loss: 0.3405 - val_accuracy: 0.9177\n",
            "Epoch 136/200\n",
            "65/65 [==============================] - 2s 25ms/step - loss: 0.4913 - accuracy: 0.7694 - val_loss: 0.3325 - val_accuracy: 0.9225\n",
            "Epoch 137/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4967 - accuracy: 0.7621 - val_loss: 0.3210 - val_accuracy: 0.9419\n",
            "Epoch 138/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4674 - accuracy: 0.7829 - val_loss: 0.3103 - val_accuracy: 0.9395\n",
            "Epoch 139/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4820 - accuracy: 0.7679 - val_loss: 0.3539 - val_accuracy: 0.8789\n",
            "Epoch 140/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4931 - accuracy: 0.7563 - val_loss: 0.3362 - val_accuracy: 0.9274\n",
            "Epoch 141/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.4960 - accuracy: 0.7573 - val_loss: 0.3368 - val_accuracy: 0.9177\n",
            "Epoch 142/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4889 - accuracy: 0.7694 - val_loss: 0.3253 - val_accuracy: 0.9346\n",
            "Epoch 143/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.4834 - accuracy: 0.7655 - val_loss: 0.3270 - val_accuracy: 0.9322\n",
            "Epoch 144/200\n",
            "65/65 [==============================] - 2s 34ms/step - loss: 0.4741 - accuracy: 0.7742 - val_loss: 0.3185 - val_accuracy: 0.9322\n",
            "Epoch 145/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4773 - accuracy: 0.7679 - val_loss: 0.3140 - val_accuracy: 0.9467\n",
            "Epoch 146/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.4753 - accuracy: 0.7699 - val_loss: 0.3227 - val_accuracy: 0.9467\n",
            "Epoch 147/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4838 - accuracy: 0.7568 - val_loss: 0.3118 - val_accuracy: 0.9467\n",
            "Epoch 148/200\n",
            "65/65 [==============================] - 2s 25ms/step - loss: 0.4559 - accuracy: 0.7907 - val_loss: 0.3017 - val_accuracy: 0.9492\n",
            "Epoch 149/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4756 - accuracy: 0.7694 - val_loss: 0.3139 - val_accuracy: 0.9274\n",
            "Epoch 150/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4874 - accuracy: 0.7641 - val_loss: 0.3170 - val_accuracy: 0.9492\n",
            "Epoch 151/200\n",
            "65/65 [==============================] - 2s 34ms/step - loss: 0.5021 - accuracy: 0.7679 - val_loss: 0.3322 - val_accuracy: 0.9419\n",
            "Epoch 152/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.4732 - accuracy: 0.7699 - val_loss: 0.3093 - val_accuracy: 0.9370\n",
            "Epoch 153/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4645 - accuracy: 0.7703 - val_loss: 0.2855 - val_accuracy: 0.9419\n",
            "Epoch 154/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4787 - accuracy: 0.7684 - val_loss: 0.3003 - val_accuracy: 0.9346\n",
            "Epoch 155/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4609 - accuracy: 0.7926 - val_loss: 0.2986 - val_accuracy: 0.9249\n",
            "Epoch 156/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4663 - accuracy: 0.7733 - val_loss: 0.3005 - val_accuracy: 0.9467\n",
            "Epoch 157/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4723 - accuracy: 0.7747 - val_loss: 0.3044 - val_accuracy: 0.9492\n",
            "Epoch 158/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.4606 - accuracy: 0.7805 - val_loss: 0.2838 - val_accuracy: 0.9564\n",
            "Epoch 159/200\n",
            "65/65 [==============================] - 2s 28ms/step - loss: 0.4676 - accuracy: 0.7733 - val_loss: 0.2989 - val_accuracy: 0.9564\n",
            "Epoch 160/200\n",
            "65/65 [==============================] - 2s 37ms/step - loss: 0.4704 - accuracy: 0.7718 - val_loss: 0.3182 - val_accuracy: 0.9128\n",
            "Epoch 161/200\n",
            "65/65 [==============================] - 2s 32ms/step - loss: 0.4783 - accuracy: 0.7665 - val_loss: 0.2923 - val_accuracy: 0.9370\n",
            "Epoch 162/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4519 - accuracy: 0.7868 - val_loss: 0.2976 - val_accuracy: 0.9346\n",
            "Epoch 163/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4631 - accuracy: 0.7791 - val_loss: 0.2877 - val_accuracy: 0.9370\n",
            "Epoch 164/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4590 - accuracy: 0.7897 - val_loss: 0.2877 - val_accuracy: 0.9516\n",
            "Epoch 165/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4728 - accuracy: 0.7747 - val_loss: 0.3079 - val_accuracy: 0.9467\n",
            "Epoch 166/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4734 - accuracy: 0.7781 - val_loss: 0.2860 - val_accuracy: 0.9564\n",
            "Epoch 167/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4554 - accuracy: 0.7868 - val_loss: 0.3051 - val_accuracy: 0.9298\n",
            "Epoch 168/200\n",
            "65/65 [==============================] - 2s 32ms/step - loss: 0.4716 - accuracy: 0.7781 - val_loss: 0.2901 - val_accuracy: 0.9395\n",
            "Epoch 169/200\n",
            "65/65 [==============================] - 2s 38ms/step - loss: 0.4690 - accuracy: 0.7800 - val_loss: 0.2789 - val_accuracy: 0.9637\n",
            "Epoch 170/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4588 - accuracy: 0.7844 - val_loss: 0.2860 - val_accuracy: 0.9370\n",
            "Epoch 171/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4533 - accuracy: 0.7815 - val_loss: 0.2762 - val_accuracy: 0.9564\n",
            "Epoch 172/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4653 - accuracy: 0.7733 - val_loss: 0.2845 - val_accuracy: 0.9443\n",
            "Epoch 173/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4583 - accuracy: 0.7989 - val_loss: 0.2685 - val_accuracy: 0.9564\n",
            "Epoch 174/200\n",
            "65/65 [==============================] - 2s 37ms/step - loss: 0.4416 - accuracy: 0.7873 - val_loss: 0.2647 - val_accuracy: 0.9613\n",
            "Epoch 175/200\n",
            "65/65 [==============================] - 3s 40ms/step - loss: 0.4820 - accuracy: 0.7737 - val_loss: 0.2884 - val_accuracy: 0.9492\n",
            "Epoch 176/200\n",
            "65/65 [==============================] - 3s 40ms/step - loss: 0.4532 - accuracy: 0.7980 - val_loss: 0.2785 - val_accuracy: 0.9443\n",
            "Epoch 177/200\n",
            "65/65 [==============================] - 2s 31ms/step - loss: 0.4550 - accuracy: 0.7941 - val_loss: 0.2670 - val_accuracy: 0.9564\n",
            "Epoch 178/200\n",
            "65/65 [==============================] - 1s 23ms/step - loss: 0.4376 - accuracy: 0.8004 - val_loss: 0.2551 - val_accuracy: 0.9588\n",
            "Epoch 179/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.4510 - accuracy: 0.7888 - val_loss: 0.2591 - val_accuracy: 0.9564\n",
            "Epoch 180/200\n",
            "65/65 [==============================] - 2s 23ms/step - loss: 0.4593 - accuracy: 0.7829 - val_loss: 0.2830 - val_accuracy: 0.9492\n",
            "Epoch 181/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4398 - accuracy: 0.7892 - val_loss: 0.2644 - val_accuracy: 0.9540\n",
            "Epoch 182/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4494 - accuracy: 0.7873 - val_loss: 0.2771 - val_accuracy: 0.9516\n",
            "Epoch 183/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4310 - accuracy: 0.8062 - val_loss: 0.2539 - val_accuracy: 0.9564\n",
            "Epoch 184/200\n",
            "65/65 [==============================] - 2s 35ms/step - loss: 0.4675 - accuracy: 0.7718 - val_loss: 0.2681 - val_accuracy: 0.9613\n",
            "Epoch 185/200\n",
            "65/65 [==============================] - 2s 34ms/step - loss: 0.4464 - accuracy: 0.7839 - val_loss: 0.2581 - val_accuracy: 0.9588\n",
            "Epoch 186/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4410 - accuracy: 0.7820 - val_loss: 0.2541 - val_accuracy: 0.9685\n",
            "Epoch 187/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4495 - accuracy: 0.7810 - val_loss: 0.2569 - val_accuracy: 0.9588\n",
            "Epoch 188/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4616 - accuracy: 0.7834 - val_loss: 0.2601 - val_accuracy: 0.9661\n",
            "Epoch 189/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4638 - accuracy: 0.7859 - val_loss: 0.2600 - val_accuracy: 0.9685\n",
            "Epoch 190/200\n",
            "65/65 [==============================] - 2s 24ms/step - loss: 0.4421 - accuracy: 0.8028 - val_loss: 0.2671 - val_accuracy: 0.9540\n",
            "Epoch 191/200\n",
            "65/65 [==============================] - 1s 22ms/step - loss: 0.4270 - accuracy: 0.8096 - val_loss: 0.2370 - val_accuracy: 0.9637\n",
            "Epoch 192/200\n",
            "65/65 [==============================] - 2s 30ms/step - loss: 0.4360 - accuracy: 0.8004 - val_loss: 0.2510 - val_accuracy: 0.9588\n",
            "Epoch 193/200\n",
            "65/65 [==============================] - 2s 36ms/step - loss: 0.4297 - accuracy: 0.7946 - val_loss: 0.2453 - val_accuracy: 0.9588\n",
            "Epoch 194/200\n",
            "65/65 [==============================] - 2s 37ms/step - loss: 0.4493 - accuracy: 0.7892 - val_loss: 0.2557 - val_accuracy: 0.9516\n",
            "Epoch 195/200\n",
            "65/65 [==============================] - 2s 30ms/step - loss: 0.4262 - accuracy: 0.8057 - val_loss: 0.2377 - val_accuracy: 0.9661\n",
            "Epoch 196/200\n",
            "65/65 [==============================] - 2s 28ms/step - loss: 0.4486 - accuracy: 0.7912 - val_loss: 0.2513 - val_accuracy: 0.9613\n",
            "Epoch 197/200\n",
            "65/65 [==============================] - 2s 31ms/step - loss: 0.4257 - accuracy: 0.7970 - val_loss: 0.2454 - val_accuracy: 0.9613\n",
            "Epoch 198/200\n",
            "65/65 [==============================] - 2s 35ms/step - loss: 0.4319 - accuracy: 0.7999 - val_loss: 0.2459 - val_accuracy: 0.9734\n",
            "Epoch 199/200\n",
            "65/65 [==============================] - 2s 26ms/step - loss: 0.4304 - accuracy: 0.7951 - val_loss: 0.2540 - val_accuracy: 0.9540\n",
            "Epoch 200/200\n",
            "65/65 [==============================] - 2s 37ms/step - loss: 0.4371 - accuracy: 0.7873 - val_loss: 0.2494 - val_accuracy: 0.9613\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.6781 - accuracy: 0.6437\n",
            "Test Loss: 0.6781\n",
            "Test Accuracy: 0.6437\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()\n",
        "print(f\"X_train shape: {X_train.shape}, y_train shape: {y_train.shape}\")\n",
        "print(f\"X_val shape: {X_val.shape}, y_val shape: {y_val.shape}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rhI6UPMVqD-2",
        "outputId": "a6048c1e-6a86-440b-8e95-56500008f14b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 8, 29, 32)         320       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 4, 14, 32)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 4, 14, 32)         0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 4, 14, 64)         18496     \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 2, 7, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 2, 7, 64)          0         \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 2, 7, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPoolin  (None, 1, 3, 128)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 1, 3, 128)         0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 384)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               49280     \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 142081 (555.00 KB)\n",
            "Trainable params: 142081 (555.00 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "X_train shape: (1651, 10, 31, 1), y_train shape: (1651,)\n",
            "X_val shape: (413, 10, 31, 1), y_val shape: (413,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_pred_prob = model.predict(X_val)\n",
        "\n",
        "y_pred = (y_pred_prob > 0.5).astype(int)\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_val, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtUK04MbqLlE",
        "outputId": "73d15775-18cc-42ba-960c-2e6f87da11bf"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 0s 6ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.94      0.96       192\n",
            "           1       0.95      0.98      0.96       221\n",
            "\n",
            "    accuracy                           0.96       413\n",
            "   macro avg       0.96      0.96      0.96       413\n",
            "weighted avg       0.96      0.96      0.96       413\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Wry-JFaOqwdM",
        "outputId": "6ef371da-30ef-437f-d2ba-a4327333f1e6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "zb3J5mnCrT-d",
        "outputId": "79d1a668-8859-4148-bae2-d99e6884a662"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}